Although the GCN is effective at node and graph-level classification, it is notoriously inefficient and unable to handle large-scale graphs [1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
The convolution operation on a graph incorporates information within neighborhoods, whose size increases with the depth of the model.
This results in high computational costs that grow exponentially with the number of layers.
Furthermore, these methods incur a high memory cost in loading the entire graph and node embeddings for the duration of training.
Thus, GCN training is very susceptible to memory overflow and the scale of experiments is quite limited (i.e., both models and graphs are restricted in size).
